Model Predictive Control

Model predictive control is a way of deciding how to steer a system by repeatedly looking ahead over a short future window and choosing the best immediate action based on that preview. At each step the controller builds a mathematical description of what will happen if it applies different possible inputs, then solves an optimisation problem that balances performance goals—such as reaching a target quickly or using little energy—with any constraints, like limits on speed or forces. The result of this calculation is a candidate sequence of control actions, but only the first one is actually applied before the whole process starts again with fresh measurements. This rolling‑horizon approach lets the controller constantly update its plan as new information arrives.

The appeal of model predictive control lies in its ability to handle multivariable systems and explicit constraints while still producing smooth, anticipatory actions. Because it explicitly reasons about what will happen several steps ahead, it can avoid sudden shocks or violations that simpler feedback laws might cause, making it especially valuable when safety or equipment wear are concerns. It also provides a systematic framework for incorporating complex objectives—like minimizing fuel consumption in an engine while keeping emissions below a regulatory cap—without resorting to ad‑hoc tuning.

You will find model predictive control in many modern machines that must operate under tight physical limits and changing conditions. Industrial plants use it to regulate temperature, pressure, or chemical reactions where overshoot could be dangerous. In the robotics world, it helps legged robots plan foot placement and balance while navigating uneven terrain, and autonomous vehicles employ it to generate smooth steering and acceleration commands that respect road geometry and traffic rules. Anywhere a system must make rapid decisions while respecting hard limits, model predictive control offers a principled way to do so.